- Conducted spatiotemporal regression analysis on homicide research data based on county health demographics, utilizing advanced statistical techniques.- Performed extensive exploratory data analysis (EDA) and feature engineering on socioeconomic factors, computing social vulnerability index to enhance model performance.- Applied natural language processing (NLP) techniques, including named entity recognition (NER), for information retrieval from news and newspaper data.- Leveraged advanced classification techniques such as CatBoost, Graph Neural Networks (GNN), LightGBM, Bagging, and K-Nearest Neighbors (KNN) to accurately classify outcome variables.- Contributed to writing a research paper, effectively communicating findings and methodologies.

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- Leveraged Foundry and predictive models to build workflows for optimizing supply chain processes, saving approximately $3.3 million in FIFO inventory costs, and created dynamic dashboarding apps for key stakeholders to provide functional reports on each workflow, enhancing data visibility and decision-making.- Integrated OpenAI's GPT-4o large language model (LLM) into an application, enabling the generation of valuable insights based on various application parameters, and enhancing the system's intelligence and user experience.- Optimized data pipelines using PySpark, resulting in a 30% reduction in processing time, significantly enhancing efficiency and performance.- Automated alert mechanisms and health checks with Power Automate, ensuring 99% system uptime and proactively identifying potential issues.

- Developed a machine learning model for a CPG client to enhance consumer surplus factor and strategic price index calculations, resulting in a 15% increase in pricing accuracy, equating to an estimated annual revenue increase of $1.5 million. Improved brand equity estimation accuracy by 18% using Ridge, Lasso and Linear Regression, strengthening the brand's competitive positioning. Additionally, deployed a web app for post modeling KPI analysis using Streamlit. - Implemented a churn rate reduction model, reducing customer churn by 71% within the first week of deployment, utilized Support Vector Classifier, Logistic Regression, and Random Forest Classifier.• Led a team of five in migrating on-premises functions to the cloud, achieving a 65% reduction in compute resources, saving the company $25,000 annually. Additionally, streamlined data processing with ETL pipelines, automating 90% of manual tasks.- Engineered a recommender engine based on Collaborative Filtering, driving a 40% boost in sales across various categories, translating to a revenue increase for a direct-to-consumer client. Achieved a 50% improvement in customer retention, contributing to an annualized revenue increase of $400,000.- Leveraged advanced forecasting techniques such as ARIMAX, XGBoost, and Exponential Smoothing to reduce stock-outs by 80%. Additionally, optimized replenishment and demand management, resulting in a cost saving of $2 million for the FMCG organization in terms of carrying costs on an annual basis.

- Developed predictive models using natural language processing to forecast consumer behavior, resulting in a 5% increase in sales revenue. Additionally, machine learning algorithms were employed to boost customer retention by 10%. Moreover, the implementation of a streamlined data entry model reduced the time spent on data entry by 85%, increasing overall operational efficiency.- Led Nextyn's technological initiatives, resulting in a significant enhancement of data-driven decision-making. This included managing the end-to-end development of the tech stack from scratch, which contributed to a substantial improvement in the company's technological capabilities.- Wrote structured, tested, readable, and maintained code that optimized machine learning and natural language processing models, resulting in a 15% improvement in model accuracy and a 20% reduction in development time.- Successfully implemented the architecture on (AWS), which led to a 30% improvement in data processing speed, enabling faster forecasting and data warehousing. This facilitated more timely reporting dashboards and data-driven decision-making for key stakeholders.- Developed and managed data pipelines to derive insights through behavioral analysis, cohort analysis, RFM analysis, and funnel analysis. These efforts led to a 25% increase in data-driven insights, resulting in efficient business strategies. Additionally, A/B testing across various use cases in the domain contributed to a 10% increase in user engagement.- Led the development and implementation of systems architecture designs, patterns, and approaches, resulting in a 20% improvement in system performance and scalability.- Utilized agile methodologies to successfully create a proprietary SaaS product from scratch using React, Node, and NoSQL databases, enhancing workflow efficiency by 30%, increasing productivity by 70% among the team members, and facilitated the seamless management of data through RESTful APIs.